By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
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March 20, 202615 min readSimplifying and clarifying the assembly code for core operations enabled automated optimization and verification. -
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February 17, 20263 min read
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January 13, 20267 min read
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Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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Machine Learning for Healthcare 20252025Large language models demonstrate impressive performance on standardized healthcare benchmarks, yet their deployment readiness for real-world environments remains poorly understood. Current medical benchmarks present idealized scenarios that misrepresent the complexity of actual clinical data. We systematically evaluate LLM robustness by introducing clinician-validated perturbations to MedQA that mirror
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Winter Simulation Conference 20252025The integration of Computer-Aided Design (CAD) models into discrete event simulation software is a critical requirement for many simulation projects, particularly those involving the movement of people or vehicles where spatial accuracy directly impacts study outcomes. While importing CAD files and configuring simulation elements is essential for system accuracy, this process is typically time-consuming
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Journal of the Royal Statistical Society, Series B2025Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we derive the finite-sample properties
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NeurIPS 2025 Workshop on Structured Probabilistic Inference & Generative Modeling2025Large Language Models (LLMs) are increasingly deployed for structured data generation, yet output consistency remains critical for production applications. We introduce a comprehensive framework for evaluating and improving consistency in LLM-generated structured outputs. Our approach combines: (1) STED (Semantic Tree Edit Distance), a novel similarity metric balancing semantic flexibility with structural
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NeurIPS 2025 Workshop on Foundations of Reasoning in Language Models2025Test-time scaling has emerged as a promising paradigm to enhance reasoning in large reasoning models by allocating additional inference-time compute. However, its potential for tabular reasoning remains underexplored. We identify that existing process reward models, widely used to supervise reasoning steps, struggle with table-specific operations such as table retrieval and schema interaction, leading to
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
